Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory226.9 KiB
Average record size in memory459.2 B

Variable types

Text2
Categorical4
Numeric7

Alerts

Fault ID has unique values Unique
Fault Location (Latitude, Longitude) has unique values Unique

Reproduction

Analysis started2025-07-23 11:51:29.101869
Analysis finished2025-07-23 11:51:41.780441
Duration12.68 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Fault ID
Text

Unique 

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.3 KiB
2025-07-23T17:21:42.314244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2024
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)100.0%

Sample

1st rowF001
2nd rowF002
3rd rowF003
4th rowF004
5th rowF005
ValueCountFrequency (%)
f001 1
 
0.2%
f018 1
 
0.2%
f005 1
 
0.2%
f006 1
 
0.2%
f007 1
 
0.2%
f008 1
 
0.2%
f009 1
 
0.2%
f010 1
 
0.2%
f011 1
 
0.2%
f012 1
 
0.2%
Other values (496) 496
98.0%
2025-07-23T17:21:43.283790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
F 506
25.0%
0 205
10.1%
1 201
 
9.9%
3 201
 
9.9%
4 201
 
9.9%
2 201
 
9.9%
5 108
 
5.3%
6 101
 
5.0%
9 100
 
4.9%
8 100
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 506
25.0%
0 205
10.1%
1 201
 
9.9%
3 201
 
9.9%
4 201
 
9.9%
2 201
 
9.9%
5 108
 
5.3%
6 101
 
5.0%
9 100
 
4.9%
8 100
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 506
25.0%
0 205
10.1%
1 201
 
9.9%
3 201
 
9.9%
4 201
 
9.9%
2 201
 
9.9%
5 108
 
5.3%
6 101
 
5.0%
9 100
 
4.9%
8 100
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 506
25.0%
0 205
10.1%
1 201
 
9.9%
3 201
 
9.9%
4 201
 
9.9%
2 201
 
9.9%
5 108
 
5.3%
6 101
 
5.0%
9 100
 
4.9%
8 100
 
4.9%

Fault Type
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size35.4 KiB
Transformer Failure
171 
Overheating
171 
Line Breakage
164 

Length

Max length19
Median length13
Mean length14.351779
Min length11

Characters and Unicode

Total characters7262
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLine Breakage
2nd rowTransformer Failure
3rd rowOverheating
4th rowLine Breakage
5th rowTransformer Failure

Common Values

ValueCountFrequency (%)
Transformer Failure 171
33.8%
Overheating 171
33.8%
Line Breakage 164
32.4%

Length

2025-07-23T17:21:43.543517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-23T17:21:43.727487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transformer 171
20.3%
failure 171
20.3%
overheating 171
20.3%
line 164
19.5%
breakage 164
19.5%

Most occurring characters

ValueCountFrequency (%)
e 1176
16.2%
r 1019
14.0%
a 841
11.6%
i 506
 
7.0%
n 506
 
7.0%
g 335
 
4.6%
335
 
4.6%
u 171
 
2.4%
t 171
 
2.4%
h 171
 
2.4%
Other values (12) 2031
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1176
16.2%
r 1019
14.0%
a 841
11.6%
i 506
 
7.0%
n 506
 
7.0%
g 335
 
4.6%
335
 
4.6%
u 171
 
2.4%
t 171
 
2.4%
h 171
 
2.4%
Other values (12) 2031
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1176
16.2%
r 1019
14.0%
a 841
11.6%
i 506
 
7.0%
n 506
 
7.0%
g 335
 
4.6%
335
 
4.6%
u 171
 
2.4%
t 171
 
2.4%
h 171
 
2.4%
Other values (12) 2031
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1176
16.2%
r 1019
14.0%
a 841
11.6%
i 506
 
7.0%
n 506
 
7.0%
g 335
 
4.6%
335
 
4.6%
u 171
 
2.4%
t 171
 
2.4%
h 171
 
2.4%
Other values (12) 2031
28.0%
Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size38.1 KiB
2025-07-23T17:21:44.283520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.76087
Min length16

Characters and Unicode

Total characters9999
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)100.0%

Sample

1st row(34.0522, -118.2437)
2nd row(34.056, -118.245)
3rd row(34.0525, -118.244)
4th row(34.055, -118.242)
5th row(34.0545, -118.243)
ValueCountFrequency (%)
118.57 3
 
0.3%
118.1422 3
 
0.3%
34.1806 2
 
0.2%
34.8522 2
 
0.2%
34.5335 2
 
0.2%
34.7943 2
 
0.2%
34.9991 2
 
0.2%
118.3633 2
 
0.2%
34.668 2
 
0.2%
118.1915 2
 
0.2%
Other values (973) 990
97.8%
2025-07-23T17:21:45.083530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1409
14.1%
. 1012
10.1%
3 931
9.3%
4 919
9.2%
8 918
9.2%
( 506
 
5.1%
, 506
 
5.1%
506
 
5.1%
- 506
 
5.1%
) 506
 
5.1%
Other values (6) 2280
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1409
14.1%
. 1012
10.1%
3 931
9.3%
4 919
9.2%
8 918
9.2%
( 506
 
5.1%
, 506
 
5.1%
506
 
5.1%
- 506
 
5.1%
) 506
 
5.1%
Other values (6) 2280
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1409
14.1%
. 1012
10.1%
3 931
9.3%
4 919
9.2%
8 918
9.2%
( 506
 
5.1%
, 506
 
5.1%
506
 
5.1%
- 506
 
5.1%
) 506
 
5.1%
Other values (6) 2280
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1409
14.1%
. 1012
10.1%
3 931
9.3%
4 919
9.2%
8 918
9.2%
( 506
 
5.1%
, 506
 
5.1%
506
 
5.1%
- 506
 
5.1%
) 506
 
5.1%
Other values (6) 2280
22.8%

Voltage (V)
Real number (ℝ)

Distinct303
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2049.6364
Minimum1800
Maximum2300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:45.368704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1800
5-th percentile1823.25
Q11923
median2058
Q32165.75
95-th percentile2276.75
Maximum2300
Range500
Interquartile range (IQR)242.75

Descriptive statistics

Standard deviation142.05415
Coefficient of variation (CV)0.069306999
Kurtosis-1.1399277
Mean2049.6364
Median Absolute Deviation (MAD)118
Skewness-0.0037190584
Sum1037116
Variance20179.38
MonotonicityNot monotonic
2025-07-23T17:21:45.680243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1982 5
 
1.0%
1912 5
 
1.0%
2092 5
 
1.0%
2164 4
 
0.8%
2076 4
 
0.8%
2206 4
 
0.8%
2077 4
 
0.8%
2270 4
 
0.8%
2201 4
 
0.8%
2106 4
 
0.8%
Other values (293) 463
91.5%
ValueCountFrequency (%)
1800 1
 
0.2%
1801 1
 
0.2%
1802 1
 
0.2%
1803 1
 
0.2%
1806 1
 
0.2%
1807 2
0.4%
1808 3
0.6%
1809 1
 
0.2%
1810 1
 
0.2%
1811 3
0.6%
ValueCountFrequency (%)
2300 1
 
0.2%
2298 3
0.6%
2296 3
0.6%
2295 1
 
0.2%
2294 2
0.4%
2292 1
 
0.2%
2290 1
 
0.2%
2289 4
0.8%
2287 2
0.4%
2284 2
0.4%

Current (A)
Real number (ℝ)

Distinct71
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.4585
Minimum180
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:45.959929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile183
Q1197.25
median218
Q3235
95-th percentile248.75
Maximum250
Range70
Interquartile range (IQR)37.75

Descriptive statistics

Standard deviation21.499787
Coefficient of variation (CV)0.099325216
Kurtosis-1.2910485
Mean216.4585
Median Absolute Deviation (MAD)19
Skewness-0.042192256
Sum109528
Variance462.24085
MonotonicityNot monotonic
2025-07-23T17:21:46.269890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185 15
 
3.0%
250 14
 
2.8%
204 13
 
2.6%
191 13
 
2.6%
247 13
 
2.6%
249 12
 
2.4%
182 12
 
2.4%
234 11
 
2.2%
246 11
 
2.2%
242 10
 
2.0%
Other values (61) 382
75.5%
ValueCountFrequency (%)
180 5
 
1.0%
181 3
 
0.6%
182 12
2.4%
183 10
2.0%
184 1
 
0.2%
185 15
3.0%
186 7
1.4%
187 7
1.4%
188 4
 
0.8%
189 5
 
1.0%
ValueCountFrequency (%)
250 14
2.8%
249 12
2.4%
248 9
1.8%
247 13
2.6%
246 11
2.2%
245 5
 
1.0%
244 4
 
0.8%
243 8
1.6%
242 10
2.0%
241 10
2.0%

Power Load (MW)
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.011858
Minimum45
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:46.514898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile45
Q147
median50
Q353
95-th percentile55
Maximum55
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1028345
Coefficient of variation (CV)0.062041976
Kurtosis-1.1760092
Mean50.011858
Median Absolute Deviation (MAD)3
Skewness-0.051041398
Sum25306
Variance9.6275819
MonotonicityNot monotonic
2025-07-23T17:21:46.706705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
52 57
11.3%
48 53
10.5%
51 53
10.5%
50 50
9.9%
46 47
9.3%
45 45
8.9%
54 45
8.9%
53 43
8.5%
55 40
7.9%
47 39
7.7%
ValueCountFrequency (%)
45 45
8.9%
46 47
9.3%
47 39
7.7%
48 53
10.5%
49 34
6.7%
50 50
9.9%
51 53
10.5%
52 57
11.3%
53 43
8.5%
54 45
8.9%
ValueCountFrequency (%)
55 40
7.9%
54 45
8.9%
53 43
8.5%
52 57
11.3%
51 53
10.5%
50 50
9.9%
49 34
6.7%
48 53
10.5%
47 39
7.7%
46 47
9.3%

Temperature (°C)
Real number (ℝ)

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.166008
Minimum20
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:46.914634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q125
median30
Q336
95-th percentile40
Maximum40
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.1183146
Coefficient of variation (CV)0.20282149
Kurtosis-1.2537328
Mean30.166008
Median Absolute Deviation (MAD)5
Skewness-0.021171028
Sum15264
Variance37.433773
MonotonicityNot monotonic
2025-07-23T17:21:47.178527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22 37
 
7.3%
32 31
 
6.1%
37 29
 
5.7%
36 28
 
5.5%
40 27
 
5.3%
25 26
 
5.1%
31 26
 
5.1%
39 25
 
4.9%
26 24
 
4.7%
21 24
 
4.7%
Other values (11) 229
45.3%
ValueCountFrequency (%)
20 17
3.4%
21 24
4.7%
22 37
7.3%
23 21
4.2%
24 21
4.2%
25 26
5.1%
26 24
4.7%
27 20
4.0%
28 21
4.2%
29 20
4.0%
ValueCountFrequency (%)
40 27
5.3%
39 25
4.9%
38 23
4.5%
37 29
5.7%
36 28
5.5%
35 20
4.0%
34 20
4.0%
33 23
4.5%
32 31
6.1%
31 26
5.1%

Wind Speed (km/h)
Real number (ℝ)

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.73913
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:47.433472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q115
median19.5
Q325
95-th percentile29
Maximum30
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8630518
Coefficient of variation (CV)0.29702685
Kurtosis-1.1632552
Mean19.73913
Median Absolute Deviation (MAD)4.5
Skewness0.076335592
Sum9988
Variance34.375377
MonotonicityNot monotonic
2025-07-23T17:21:47.666746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
13 37
 
7.3%
18 31
 
6.1%
15 31
 
6.1%
22 30
 
5.9%
14 29
 
5.7%
28 28
 
5.5%
16 25
 
4.9%
23 25
 
4.9%
24 24
 
4.7%
25 23
 
4.5%
Other values (11) 223
44.1%
ValueCountFrequency (%)
10 23
4.5%
11 20
4.0%
12 13
 
2.6%
13 37
7.3%
14 29
5.7%
15 31
6.1%
16 25
4.9%
17 21
4.2%
18 31
6.1%
19 23
4.5%
ValueCountFrequency (%)
30 17
3.4%
29 22
4.3%
28 28
5.5%
27 18
3.6%
26 22
4.3%
25 23
4.5%
24 24
4.7%
23 25
4.9%
22 30
5.9%
21 22
4.3%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size31.8 KiB
Rainy
117 
Clear
110 
Thunderstorm
100 
Snowy
92 
Windstorm
87 

Length

Max length12
Median length5
Mean length7.0711462
Min length5

Characters and Unicode

Total characters3578
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowRainy
3rd rowWindstorm
4th rowClear
5th rowSnowy

Common Values

ValueCountFrequency (%)
Rainy 117
23.1%
Clear 110
21.7%
Thunderstorm 100
19.8%
Snowy 92
18.2%
Windstorm 87
17.2%

Length

2025-07-23T17:21:47.951068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-23T17:21:48.149442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rainy 117
23.1%
clear 110
21.7%
thunderstorm 100
19.8%
snowy 92
18.2%
windstorm 87
17.2%

Most occurring characters

ValueCountFrequency (%)
r 397
 
11.1%
n 396
 
11.1%
o 279
 
7.8%
a 227
 
6.3%
e 210
 
5.9%
y 209
 
5.8%
i 204
 
5.7%
t 187
 
5.2%
m 187
 
5.2%
d 187
 
5.2%
Other values (10) 1095
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 397
 
11.1%
n 396
 
11.1%
o 279
 
7.8%
a 227
 
6.3%
e 210
 
5.9%
y 209
 
5.8%
i 204
 
5.7%
t 187
 
5.2%
m 187
 
5.2%
d 187
 
5.2%
Other values (10) 1095
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 397
 
11.1%
n 396
 
11.1%
o 279
 
7.8%
a 227
 
6.3%
e 210
 
5.9%
y 209
 
5.8%
i 204
 
5.7%
t 187
 
5.2%
m 187
 
5.2%
d 187
 
5.2%
Other values (10) 1095
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 397
 
11.1%
n 396
 
11.1%
o 279
 
7.8%
a 227
 
6.3%
e 210
 
5.9%
y 209
 
5.8%
i 204
 
5.7%
t 187
 
5.2%
m 187
 
5.2%
d 187
 
5.2%
Other values (10) 1095
30.6%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size32.4 KiB
Completed
173 
Scheduled
170 
Pending
163 

Length

Max length9
Median length9
Mean length8.3557312
Min length7

Characters and Unicode

Total characters4228
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowScheduled
2nd rowCompleted
3rd rowPending
4th rowCompleted
5th rowScheduled

Common Values

ValueCountFrequency (%)
Completed 173
34.2%
Scheduled 170
33.6%
Pending 163
32.2%

Length

2025-07-23T17:21:48.415502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-23T17:21:48.599551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
completed 173
34.2%
scheduled 170
33.6%
pending 163
32.2%

Most occurring characters

ValueCountFrequency (%)
e 849
20.1%
d 676
16.0%
l 343
 
8.1%
n 326
 
7.7%
C 173
 
4.1%
o 173
 
4.1%
m 173
 
4.1%
p 173
 
4.1%
t 173
 
4.1%
S 170
 
4.0%
Other values (6) 999
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 849
20.1%
d 676
16.0%
l 343
 
8.1%
n 326
 
7.7%
C 173
 
4.1%
o 173
 
4.1%
m 173
 
4.1%
p 173
 
4.1%
t 173
 
4.1%
S 170
 
4.0%
Other values (6) 999
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 849
20.1%
d 676
16.0%
l 343
 
8.1%
n 326
 
7.7%
C 173
 
4.1%
o 173
 
4.1%
m 173
 
4.1%
p 173
 
4.1%
t 173
 
4.1%
S 170
 
4.0%
Other values (6) 999
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 849
20.1%
d 676
16.0%
l 343
 
8.1%
n 326
 
7.7%
C 173
 
4.1%
o 173
 
4.1%
m 173
 
4.1%
p 173
 
4.1%
t 173
 
4.1%
S 170
 
4.0%
Other values (6) 999
23.6%

Component Health
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size31.9 KiB
Normal
174 
Overheated
173 
Faulty
159 

Length

Max length10
Median length6
Mean length7.3675889
Min length6

Characters and Unicode

Total characters3728
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowFaulty
3rd rowOverheated
4th rowNormal
5th rowFaulty

Common Values

ValueCountFrequency (%)
Normal 174
34.4%
Overheated 173
34.2%
Faulty 159
31.4%

Length

2025-07-23T17:21:48.832810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-23T17:21:49.017881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 174
34.4%
overheated 173
34.2%
faulty 159
31.4%

Most occurring characters

ValueCountFrequency (%)
e 519
13.9%
a 506
13.6%
r 347
9.3%
l 333
8.9%
t 332
8.9%
N 174
 
4.7%
o 174
 
4.7%
m 174
 
4.7%
O 173
 
4.6%
v 173
 
4.6%
Other values (5) 823
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 519
13.9%
a 506
13.6%
r 347
9.3%
l 333
8.9%
t 332
8.9%
N 174
 
4.7%
o 174
 
4.7%
m 174
 
4.7%
O 173
 
4.6%
v 173
 
4.6%
Other values (5) 823
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 519
13.9%
a 506
13.6%
r 347
9.3%
l 333
8.9%
t 332
8.9%
N 174
 
4.7%
o 174
 
4.7%
m 174
 
4.7%
O 173
 
4.6%
v 173
 
4.6%
Other values (5) 823
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 519
13.9%
a 506
13.6%
r 347
9.3%
l 333
8.9%
t 332
8.9%
N 174
 
4.7%
o 174
 
4.7%
m 174
 
4.7%
O 173
 
4.6%
v 173
 
4.6%
Other values (5) 823
22.1%

Duration of Fault (hrs)
Real number (ℝ)

Distinct41
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0081028
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:49.243346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.3
Q13
median4
Q35
95-th percentile5.8
Maximum6
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1472712
Coefficient of variation (CV)0.28623797
Kurtosis-1.1938123
Mean4.0081028
Median Absolute Deviation (MAD)1
Skewness0.024983872
Sum2028.1
Variance1.3162312
MonotonicityNot monotonic
2025-07-23T17:21:49.532682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
5.8 19
 
3.8%
2.6 18
 
3.6%
5.2 17
 
3.4%
5.1 17
 
3.4%
3.3 17
 
3.4%
2.5 16
 
3.2%
2.3 16
 
3.2%
2.9 16
 
3.2%
4.6 16
 
3.2%
3.4 15
 
3.0%
Other values (31) 339
67.0%
ValueCountFrequency (%)
2 4
 
0.8%
2.1 8
1.6%
2.2 11
2.2%
2.3 16
3.2%
2.4 10
2.0%
2.5 16
3.2%
2.6 18
3.6%
2.7 11
2.2%
2.8 13
2.6%
2.9 16
3.2%
ValueCountFrequency (%)
6 8
1.6%
5.9 14
2.8%
5.8 19
3.8%
5.7 9
1.8%
5.6 6
 
1.2%
5.5 12
2.4%
5.4 11
2.2%
5.3 5
 
1.0%
5.2 17
3.4%
5.1 17
3.4%

Down time (hrs)
Real number (ℝ)

Distinct61
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9998024
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-23T17:21:49.848626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.3
Q12.3
median4
Q35.7
95-th percentile6.7
Maximum7
Range6
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation1.8423672
Coefficient of variation (CV)0.46061455
Kurtosis-1.355188
Mean3.9998024
Median Absolute Deviation (MAD)1.7
Skewness0.040539268
Sum2023.9
Variance3.3943168
MonotonicityNot monotonic
2025-07-23T17:21:50.336095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 15
 
3.0%
6.6 13
 
2.6%
1.9 12
 
2.4%
6.3 12
 
2.4%
1.8 12
 
2.4%
6.9 12
 
2.4%
2 12
 
2.4%
2.5 12
 
2.4%
1.1 12
 
2.4%
2.8 11
 
2.2%
Other values (51) 383
75.7%
ValueCountFrequency (%)
1 4
 
0.8%
1.1 12
2.4%
1.2 7
1.4%
1.3 4
 
0.8%
1.4 11
2.2%
1.5 15
3.0%
1.6 7
1.4%
1.7 9
1.8%
1.8 12
2.4%
1.9 12
2.4%
ValueCountFrequency (%)
7 6
1.2%
6.9 12
2.4%
6.8 7
1.4%
6.7 11
2.2%
6.6 13
2.6%
6.5 10
2.0%
6.4 10
2.0%
6.3 12
2.4%
6.2 9
1.8%
6.1 10
2.0%

Interactions

2025-07-23T17:21:39.616755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:29.994274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:31.598547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:33.195558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:34.643480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:36.473707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:37.951131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:39.828118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:30.218945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:31.807953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:33.400234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:34.877768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:36.666586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:38.182936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:40.033518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:30.455672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:32.035092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:33.549910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:35.116248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:36.851954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:38.434975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:40.239838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:30.657867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:32.251381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:33.742924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:35.334225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:37.060099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:38.619654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:40.473632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:30.890126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:32.501483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:33.968911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:35.743585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:37.323909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:38.873734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:40.691473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:31.124644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:32.735254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:34.201996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:35.982216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:37.497512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:39.140233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:40.903443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:31.380038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:32.966903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:34.435877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:36.246407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:37.728235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-23T17:21:39.367888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-23T17:21:50.550081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Component HealthCurrent (A)Down time (hrs)Duration of Fault (hrs)Fault TypeMaintenance StatusPower Load (MW)Temperature (°C)Voltage (V)Weather ConditionWind Speed (km/h)
Component Health1.0000.0250.0000.0240.0630.0000.0000.0690.0000.0000.000
Current (A)0.0251.000-0.0710.0280.0000.0390.0760.0270.0960.0000.059
Down time (hrs)0.000-0.0711.0000.0220.0720.056-0.0140.0530.0130.0000.096
Duration of Fault (hrs)0.0240.0280.0221.0000.0000.0590.062-0.0530.0170.075-0.068
Fault Type0.0630.0000.0720.0001.0000.0000.1050.0000.0000.0000.040
Maintenance Status0.0000.0390.0560.0590.0001.0000.0380.0000.0000.0640.089
Power Load (MW)0.0000.076-0.0140.0620.1050.0381.0000.0900.0400.011-0.033
Temperature (°C)0.0690.0270.053-0.0530.0000.0000.0901.000-0.0360.000-0.059
Voltage (V)0.0000.0960.0130.0170.0000.0000.040-0.0361.0000.038-0.035
Weather Condition0.0000.0000.0000.0750.0000.0640.0110.0000.0381.0000.000
Wind Speed (km/h)0.0000.0590.096-0.0680.0400.089-0.033-0.059-0.0350.0001.000

Missing values

2025-07-23T17:21:41.244296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-23T17:21:41.571458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Fault IDFault TypeFault Location (Latitude, Longitude)Voltage (V)Current (A)Power Load (MW)Temperature (°C)Wind Speed (km/h)Weather ConditionMaintenance StatusComponent HealthDuration of Fault (hrs)Down time (hrs)
0F001Line Breakage(34.0522, -118.2437)2200250502520ClearScheduledNormal2.01.0
1F002Transformer Failure(34.056, -118.245)1800180452815RainyCompletedFaulty3.05.0
2F003Overheating(34.0525, -118.244)2100230553525WindstormPendingOverheated4.06.0
3F004Line Breakage(34.055, -118.242)2050240482310ClearCompletedNormal2.53.0
4F005Transformer Failure(34.0545, -118.243)1900190503018SnowyScheduledFaulty3.54.0
5F006Overheating(34.05, -118.24)2150220523222ThunderstormPendingOverheated5.07.0
6F007Line Breakage(34.9449, -118.9839)1994233512321SnowyCompletedNormal3.76.1
7F008Transformer Failure(34.2294, -118.2988)2133229522018SnowyScheduledNormal5.42.1
8F009Line Breakage(34.1279, -118.8442)2155240452129RainyPendingOverheated3.24.7
9F010Line Breakage(34.4192, -118.8254)2065199552521ClearScheduledNormal4.02.8
Fault IDFault TypeFault Location (Latitude, Longitude)Voltage (V)Current (A)Power Load (MW)Temperature (°C)Wind Speed (km/h)Weather ConditionMaintenance StatusComponent HealthDuration of Fault (hrs)Down time (hrs)
496F497Line Breakage(34.8438, -118.1141)1855191502523ClearPendingNormal5.02.4
497F498Transformer Failure(34.0532, -118.4305)1902185453412WindstormPendingFaulty4.71.0
498F499Transformer Failure(34.2564, -118.2993)2134191533819ThunderstormPendingFaulty4.82.4
499F500Overheating(34.1408, -118.4325)2039232543728RainyCompletedFaulty2.52.5
500F501Transformer Failure(34.766, -118.5866)2175191543614WindstormScheduledFaulty5.21.5
501F502Overheating(34.7734, -118.9645)1970185473127ThunderstormCompletedNormal4.71.4
502F503Transformer Failure(34.2133, -118.1184)2204226522826RainyCompletedNormal3.71.4
503F504Transformer Failure(34.979, -118.5646)2181234522628ThunderstormScheduledOverheated6.06.3
504F505Overheating(34.5034, -118.4528)2295202502722SnowyCompletedNormal4.23.0
505F506Line Breakage(34.4455, -118.5557)1941186513124ThunderstormCompletedFaulty2.36.8